Application of an unsteady state environmental distribution model to a decadal time series of PAH concentrations in Central Europe.

To explain observed decadal trends in concentrations of polycyclic aromatic hydrocarbons (PAHs) in soil at the Kosetice observatory, Czech Republic, an environmental distribution model for persistent organic pollutants (POPs) based on the fugacity approach was developed. Weekly air concentrations were used as input data for the unsteady state model and concentrations in soil were calculated. In general, agreement between measured and predicted soil concentrations of PAHs was observed. Temporal trends in PAH concentrations in Kosetice can be related to changes in residential heating. Predicted soil concentrations of volatile PAHs namely acenaphthylene, fluorene and phenanthrene are in better correspondence with observed data than concentrations of less volatile PAHs i.e. dibenzo(ah)anthracene, benzo(a)pyrene and benzo(ghi)perylene. These discrepancies between model results and field data are probably a result of a simplified description of degradation and aging processes in soil. The results from our dynamic multicompartmental model confirmed our hypothesis about unsteady state conditions between the air and soil, and suggested that a commonly used simple steady state model should be only applied as a predictive tool in a small region when local sources and sinks are well described.

Spatially resolved distribution models of POP concentrations in soil: a stochastic approach using regression trees.

Background concentrations of selected persistent organic pollutants (polychlorinated biphenyls, hexachlorobenzene, p,p'-DDT including metabolites) and polyaromatic hydrocarbons in soils of the Czech Republic were predicted in this study, and the main factors affecting their geographical distribution were identified. A database containing POP concentrations in 534 soil samples and the set of specific environmental predictors were used for development of a model based on regression trees. Selected predictors addressed specific conditions affecting a behavior of the individual groups of pollutants: a presence of primary and secondary sources, density of human settlement, geographical characteristics and climatic conditions, land use, land cover, and soil properties. The model explained a high portion of variability in relationship between the soil concentrations of selected organic pollutants and available predictors. A tree for hexachlorobenzene was the most successful with 76.2% of explained variability, followed by trees for polyaromatic hydrocarbons (71%), polychlorinated biphenyls (68.6%), and p,p'-DDT and metabolites (65.4%). The validation results confirmed that the model is stable, general and useful for prediction. The stochastic model applied in this study seems to be a promising tool capable of predicting the environmental distribution of organic pollutants.